Bag of Words + Num. Feat.(Black Box):  Following [1]}, we build a black box model for the double task estimation of ERR and word count.The word count estimation task was treated as a 46 class classification model with class 1 corresponding to word count of 2, class 46 with word count of 47 (90th percentile). Similarly, the ERR estimation task was modelled as a 20 class classification problem with class 0 corresponding to no errors and class 19 corresponding to 19 errors(90th percentile). Both of these tasks are handled by logistic regression models which use Bag of Words features for both the words present in the sentence as well as the graphemes (monograms and bigrams) of the sentence. These features are concatenated with numerical features such as word count, grapheme count and the duration and then fed to a feedforward network. Dropouts are also added after each layer to prevent overfitting.
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